Challenges Associated with Creeping Disasters in Disaster Risk Science and Practice: Considering Disaster Onset Dynamics

In this article, we set out to reconcile a general conceptualization of disaster temporalities by drawing on the epitome example of a creeping disaster, namely famine. Our argument is driven by the recognition that slowly manifesting disaster impacts pose distinct challenges for decision makers and researchers while there is a tendency for the disaster literature to overlook the role of disaster onset dynamics. More specifically and as a starting point, we identify four key themes that merit particular attention when dealing with creeping disasters: (1) our understanding of disaster as a phenomenon; (2) measurement and operationalization; (3) early warning and response; and (4) disaster management and termination. By integrating conceptual discussions of disaster with famine scholarship—a phenomenon often excluded from mainstream disaster research—this article provides fresh perspectives on disaster science as well as a number of implications for how we think about disaster risk reduction.

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

The importance of social work roles in disaster risk management in Zimbabwe

This study was aimed at exploring the efficacy of social work roles in disaster risk management in Zimbabwe in the face of climatic changes. It was carried out in Binga district, Matebeleland-North province using the qualitative methodology and a case study design. The study sample was comprised of 8 practicing social workers. Snowballing and purposive sampling techniques were used to select these participants. Data were collected through in-depth interviews using an interview guide. Thematic content analysis was employed to analyze the collected data. It was found that social workers play essential roles before, during and after disasters. On disaster preparedness, it was established that social workers assume roles of educators, community workers and disaster planners. Moreover, it was found that they play the roles of case managers, administrators of social protection and counselors during the disaster response phase of disaster risk management. Subsequently they also act as advocates, development facilitators as well as monitoring and evaluation personnel in the disaster recovery phase. As per the findings, the study recommends that social workers be recognized as essential in disaster risk management at both policy and practice level. Further, there is need to enhance social work training education for flexibility and contextual application of social work knowledge in the field of disaster risk management for quality realization of green social work in Zimbabwe.

A Predictive Model of Pandemic Disaster Fear Caused by Coronavirus (COVID-19): Implications for Decision-Makers

This paper presents quantitative research results regarding a predictive model of pandemic disaster fear caused by the coronavirus disease (COVİD-19). The aim of this paper was to establish the level and impact of certain demographic and socioeconomic characteristics on pandemic disaster fear caused by the coronavirus (COVID-19). The research was conducted using a questionnaire that was provided and then collected online for 1226 respondents during May 2021. A closed, five-point Likert scale was used to create the structured questionnaire. The first section of the questionnaire included research questions about the participants’ socioeconomic and demographic characteristics, while the second section included issue questions about fear caused by COVID-19. The results of multivariate regression analyses showed the most important predictor for fear of COVID-19 to be gender, followed by age and education level. Furthermore, the results of t-tests showed statistically significant differences between men and women in terms of different aspects of pandemic disaster fear caused by the coronavirus disease. Our results have several significant public health implications. Women who were more educated and knowledgeable, married, and older, reported a greater fear of the outbreak at various levels. Decision-makers can use these findings to identify better strategic opportunities for pandemic disaster risk management.